Your next competitive edge? Build it yourself.
Explore custom plugins and AI agents built around the way your business actually works.
What is the difference between a plugin and an agent?
A storefront plugin adds a defined capability: a bundle builder, product quiz or custom delivery selector. An agent uses instructions, context and connected tools to work through a task. A shopping agent, for example, might ask a follow-up question before recommending a suitable product.
Both should start with a specific customer or team need. The point is not to add AI to every interaction. It is to make a useful job easier, with a clear boundary around what the feature can read or change.
Which custom storefront features are worth building?
Look for workarounds your customers already use. If shoppers message you to assemble a gift set, a bundle builder may be helpful. If buyers repeatedly ask which part fits their model, a compatibility finder could remove uncertainty. If your products are made to order, a clear configuration flow might be more useful than a generic product form.
Describe the inputs, expected outcome and exceptions before asking AI to develop the feature. A bundle needs rules for eligible items, stock, discounts and returns. A quiz needs a way to explain why it recommends a product. Test those rules with real catalogue examples.
How do you design your own ecommerce agent?
Give the agent a narrow job and define a successful result. A content assistant could prepare a product description using approved specifications and a saved brand voice. A shopping guide could compare in-stock products using the customer’s stated preferences. An inventory helper could summarise items for a staff review.
Decide which tools and data it needs. A shopping guide should not need access to payroll. A draft-writing assistant should not automatically send marketing messages. External services may require credentials, an integration and usage budgets, so include those requirements in the development brief.
Keep approvals close to important actions
Separate drafting and recommending from actions that change the business. Price changes, refunds, bulk edits and outbound messages deserve an explicit approval step. Explain what the agent plans to do and let a person inspect the details first.
Test missing data, ambiguous requests, unavailable products and integration failures. The agent should be able to say it does not have enough information and hand the task back. Record enough context for a team member to understand what happened without exposing unnecessary customer data.
How does this fit the Markit development workflow?
Markit’s AI storefront workflow supports development through conversation, preview and publishing. Use that workflow to prototype a custom extension, inspect it in context and refine it. Plugins and agents described here are project ideas, not a claim that every example is available as a ready-made integration.
Start with one small feature and an observable outcome. Launch after testing the full journey, listen to users and expand only when the next step solves a real problem. Your store should grow more useful, not merely more complicated.